Numeric precision in Microsoft Excel: Difference between revisions

Revision as of 21:52, 23 March 2019

Revision as of 21:54, 23 March 2019

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A different field where accuracy is an issue is the area of [[Numerical integration|numerical computing of integrals]] and the [[Numerical ordinary differential equations|solution of differential equations]]. Examples are [[Simpson's rule]], the [[Runge–Kutta method]], and the Numerov algorithm for the [[Schrödinger equation]].<ref name=Blom>

[httphttps://www.teorfysresearchgate.lu.senet/personalprofile/Anders.BlomAnders_Blom5/publication/242226580_Computer_algorithms_for_solving_the_Schrodinger_and_Poisson_equations/links/useful55a1d42a08aec9ca1e63e3a5/scrComputer-algorithms-for-solving-the-Schrodinger-and-Poisson-equations.pdf Anders Blom] ''Computer algorithms for solving the Schrödinger and Poisson equations'', Department of Physics, Lund University, 2002.

</ref> Using Visual Basic for Applications, any of these methods can be implemented in Excel. Numerical methods use a grid where functions are evaluated. The functions may be interpolated between grid points or extrapolated to locate adjacent grid points. These formulas involve comparisons of adjacent values. If the grid is spaced very finely, round-off error will occur, and the less the precision used, the worse the round-off error. If spaced widely, accuracy will suffer. If the numerical procedure is thought of as a [[Negative feedback amplifier|feedback system]], this calculation noise may be viewed as a signal that is applied to the system, which will lead to instability unless the system is carefully designed.<ref name=Hamming>